A two-stage classification technique for bankruptcy prediction

European Journal of Operational Research

Volumn 254, Issue 1, 2016, Pages 236-252

  Du Jardin, Philippe ^a  

^a EDHEC BUSINESS SCHOOL   (France)

Author keywords

Bankruptcy; Decision support systems; Finance; Financial profile; Forecasting

Indexed keywords

ARTIFICIAL INTELLIGENCE; DECISION SUPPORT SYSTEMS; FINANCE;

BANKRUPTCY; BANKRUPTCY PREDICTION; CLASSIFICATION TECHNIQUE; DESIGN MODELS; ENSEMBLE TECHNIQUES; EXPLICIT KNOWLEDGE; FINANCIAL PROFILES; PREDICTION RULES;

FORECASTING;

EID: 84979462408     PISSN: 03772217     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ejor.2016.03.008     Document Type: Article

References (85)

1. Alfaro E., Garcia N., Games M., and Elizondo D. Bankruptcy forecasting: an empirical comparison of AdaBoost and neural networks Decision Support Systems 45 2008 110 122
2. Altman E.I. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy Journal of Finance 23 1968 589 609
3. Atiya A. Bankruptcy prediction for credit risk using neural networks: a survey and new results IEEE Transactions on Neural Networks 7 2001 929 935
4. Aziz A., Emanuel D.C., and Lawson G.C. Bankruptcy prediction: an investigation of cash flow based models Journal of Management Studies 25 1988 419 437
5. Balcaen S., and Ooghe H. 35 years of studies on business failure: an overview of the classical statistical methodologies and their related problems British Accounting Review 38 2006 63 93
6. Banasik J., Crook J.N., and Thomas L.C. Does scoring a sub-population make a difference? International Review of Retail Distribution and Consumer Research 6 1996 180 195
7. Bardos M. Detecting the risk of company failure at the banque de france Journal of Banking and Finance 22 1998 1405 1419
8. Bardos M. What is at stake in the construction and use of credit scores? Computational Economics 29 2007 159 172
9. Bijak K., and Thomas L.C. Does segmentation always improve model performance in credit scoring? Expert Systems with Applications 39 2012 2433 2442
10. Breiman L. Bagging predictors Machine Learning 24 1996 123 140
11. Breiman L., Friedman J.H., Olshen R.A., and Stone C.J. Classification and regression trees 1984 Chapman and Hall-CRC Boca Raton, Florida
12. Bühlmann P., and Yu B. Analyzing bagging The Annals of Statistics 30 2002 927 961
13. Chandler, G. G., & Ewert, D. C. (1976). Does segmentation always improve model performance in credit scoring?.
14. Cho S., Kim J., and Bae J.K. An integrative model with subject weight based on neural network learning for bankruptcy prediction Expert Systems with Applications 36 2009 403 410
15. Chuang C.L. Application of hybrid case-based reasoning for enhanced performance in bankruptcy prediction Information Sciences 236 2013 174 185
16. Croux C., Joossens K., and Lemmens A. Trimmed bagging Computational Statistics and Data Analysis 52 2007 362 368
17. D'Aveni R.A. The aftermath of organizational decline: a longitudinal study of the strategic and managerial characteristics of declining firms Academy of Management Journal 32 1989 577 605
18. Demiriz A., Bennett K.P., and Taylor J.S. Linear programming boosting via column generation Machine Learning 46 2002 225 254
19. Dimitras A.I., Zanakis S., and Zopounidis C. A survey of business failures with an emphasis on prediction methods and industrial applications European Journal of Operational Research 90 1996 487 513
20. Domingo C., and Watanabe O. MadaBoost: A modification of AdaBoost ACM 13th annual conference on computational learning theory 2000 Morgan Kaufmann Publishers Inc., San Francisco, California 180 189
21. Fedorova E., Gilenko E., and Dovzhenko S. Bankruptcy prediction for Russian companies: application of combined classifiers Expert Systems with Applications 40 2013 7285 7293
22. Finlay S. Multiple classifier architectures and their application to credit risk assessment European Journal of Operational Research 210 2011 368 378
23. Freund Y. Boosting a weak learning algorithm by majority Colt'90: The third annual workshop on computational learning theory 1990 202 216
24. Freund Y., and Schapire R.E. Experiments with a new boosting algorithm 13th international conference on machine learning 1996 148 156
25. Friedman J., Hastie T., and Tibshirani R. Additive logistic regression: a statistical view of boosting Annals of Statistics 28 2000 337 374
26. Friedman J., Hastie T., and Tibshirani R. An adaptive version of the boost by majority algorithm Machine Learning 43 2001 293 318
27. Frydman H., Altman E., and Kao D. Introducing recursive partitioning for financial classification: the case of financial distress Journal of Finance 40 1985 269 291
28. Gamma J., and Brazdil P. Cascade generalization Machine Learning 41 2000 315 343
29. Garcia-Pedrajas N., and Ortiz-Boyer D. Boosting random subspace method Neural Networks 21 2008 1344 1362
30. Geng R., Bose I., and Chen X. Prediction of financial distress: an empirical study of listed Chinese companies using data mining European Journal of Operational Research 241 2015 236 247
31. Grandvallet Y. Bagging can stabilize without reducing variance International conference on artificial neural networks. Vienna, Austria 2001 49 56
32. Hand D.J. Measuring classifier performance: a coherent alternative to the area under the ROC curve Machine Learning 77 2009 103 123
33. He H., and Garcia E.A. Learning from imbalanced data IEEE Transactions on Knowledge and Data Engineering 21 2009 1263 1284
34. Heo J., and Yang J.Y. AdaBoost based bankruptcy forecasting of korean construction companies Applied Soft Computing 24 2014 494 499
35. Ho T.K. The random subspace method for constructing decision forests IEEE Transactions on Pattern Analysis and Machine Intelligence 20 1998 832 844
36. du Jardin P. Bankruptcy prediction using terminal failure processes European Journal of Operational Research 242 2015 286 303
37. Kaski S., and Lagus K. Comparing self-organizing maps J.C.V.C. von der Malsburg, W. von Seelen, B. Sendhoff, International conference on artificial neural networks Lecture Notes in Computer Science 1112 1996 Springer, Berlin, Heidelberg 809 814
38. Kim M.J., and Kang D.K. Ensemble with neural networks for bankruptcy prediction Expert Systems with Applications 37 2010 3373 3379
39. Kim M.J., Kang D.K., and Kim H.B. Geometric mean based boosting algorithm with over-sampling to resolve data imbalance problem for bankruptcy prediction Expert Systems with Applications 42 2013 1074 1082
40. Kim S.Y., and Upneja A. Predicting restaurant financial distress using decision tree and AdaBoosted decision tree models Economic Modelling 36 2014 354 362
41. Kohonen T. Self-organizing maps 3rd Ed. Springer Series in Information Sciences 30 2001 Springer
42. Kuncheva L.I. That elusive diversity in classifier ensembles 1st iberian conference on pattern recognition and image analysis 2003 Federal Reserve Bank 1126 1138
43. Kuncheva L.I., Skurichina M., and Duin R.P.W. An experimental study on diversity for bagging and boosting with linear classifiers Information Fusion 3 2002 245 258
44. Lai C., Reinders M.J., and Wessels L. Random subspace method for multivariate feature selection Pattern Recognition Letters 27 2006 1067 1076
45. Laitinen T. Financial ratios and different failure processes Journal of Business Finance and Accounting 18 1991 649 673
46. Lensberg T., Eilifsen A., and McKee T.E. Bankruptcy theory development and classification via genetic programming European Journal of Operational Research 169 2006 677 697
47. Leray P., and Gallinari P. Feature selection with neural networks Behaviormetrika 26 1998 145 166
48. Leshno M., and Spector Y. Neural network prediction analysis: the bankruptcy case Neurocomputing 10 1996 125 147
49. Li H., Lee Y.C., Zhou Y.C., and Sun J. The random subspace binary logit (RSBL) model for bankruptcy prediction Knowledge-Based Systems 24 2011 1380 1388
50. Li H., and Sun J. Majority voting combination of multiple case-based reasoning for financial distress prediction Expert Systems with Applications 36 2009 4363 4373
51. Li H., and Sun J. Business failure prediction using hybrid2 case-based reasoning (H2CBR) Computers and Operations Research 37 2010 137 151
52. Li H., and Sun J. Principal component case-based reasoning ensemble for business failure prediction Information and Management 48 2011 220 227
53. Liao J.J., Shih C.H., Chen T.F., and Hsu M.F. An ensemble-based model for two-class imbalanced financial problem Economic Modelling 37 2014 175 183
54. Lim M.K., and Sohn S.Y. Cluster-based dynamic scoring model Expert Systems with Applications 32 2007 427 431
55. Marques A.I., Garcia V., and Sanchez J.S. Exploring the behaviour of base classifiers in credit scoring ensembles Expert Systems with Applications 39 2012 10244 10250
56. Mensah Y.M. An examination of the stationarity of multivariate bankruptcy prediction models: a methodological study Journal of Accounting Research 22 1984 380 395
57. Miller D., and Friesen P.H. Strategy-making in context: ten empirical archetypes Journal of Management Studies 14 1977 253 280
58. Milligan G.W. A monte-carlo study of thirty internal criterion measures for cluster analysis Psychometrika 46 1981 187 199
59. Moulton W.N., and Thomas H. Business failure pathways: environmental stress and organizational response Journal of Management 22 1996 571 595
60. Neves J.C., and Vieira A. Improving bankruptcy prediction with hidden layer learning vector quantization European Accounting Review 15 2000 253 271
61. Ohlson J.A. Financial ratios and the probabilistic prediction of bankruptcy Journal of Accounting Research 18 1980 109 131
62. Panov P., and Dzeroski S. Combining bagging and random subspaces to create better ensembles Advances in intelligent data analysis vii Lecture Notes in Computer Science 4723 2007
63. Platt H.D., and Platt M.B. Predicting corporate financial distress: reflections on choice-based sample bias Journal of Economics and Finance 26 2002 184 199
64. Psillaki M., Tsolas I.E., and Margaritis D. Evaluation of credit risk based on firm performance European Journal of Operational Research 201 2010 873 881
65. Ravi-Kumar P., and Ravi V. Bankruptcy prediction in banks and firms via statistical and intelligent techniques - a review European Journal of Operational Research 180 2007 1 28
66. Schapire R.E. The strength of weak learnability Machine Learning 5 1990 197 227
67. Serrano-Cinca C. Self-organizing neural networks for financial diagnosis Decision Support Systems 17 1996 227 238
68. Skurichina M., and Duin B. Bagging for linear classifiers Pattern Recognition 31 1998 909 930
69. Skurichina M., and Duin R.P.W. Bagging, boosting and the random subspace method for linear classifiers Pattern Analysis and Applications 5 2002 121 135
70. Stein R.M. Benchmarking default prediction models: pitfalls and remedies in model validation Journal of Risk Model Validation 1 2007 77 113
71. Sun J., Jia M.Y., and Li H. AdaBoost ensemble for financial distress prediction: an empirical comparison with data from Chinese listed companies Expert Systems with Applications 38 2011 9305 9312
72. Sun J., and Li H. Financial distress prediction based on serial combination of multiple classifiers Expert Systems with Applications 36 2009 8659 8666
73. Sun J., and Li H. Financial distress prediction using support vector machines: ensemble vs. individual Applied Soft Computing 12 2012 2254 2265
74. Sun J., Li H., Huang Q.-H., and He K.-Y. Predicting financial distress and corporate failure: a review from the state-of-the-art definitions, modeling, sampling, and featuring approaches Knowledge-Based Systems 57 2014 41 56
75. Sun Y., Kamel M.S., Wong A.K., and Wang Y. Cost-sensitive boosting for classification of imbalanced data Pattern Recognition 40 2007 3358 3378
76. Tian S., Yu Y., and Zhou M. Data sample selection issues for bankruptcy prediction Risk, Hazards & Crisis in Public Policy 6 2015 91 116
77. Tsai C.F. Combining cluster analysis with classifier ensembles to predict financial distress Information Fusion 16 2014 46 58
78. Tumer K., and Ghosh J. Analysis of decision boundaries in linearly combined neural classifiers Pattern Recognition 29 1996 341 348
79. Warmuth M.K., Liao J., and Ratsch G. Totally corrective boosting algorithms that maximize the margin Icml'06: The 23rd international conference on machine learning 2006 New York 1001 1008
80. West D., Dellana S., and Qian J. Neural network ensemble strategies for financial decision application Computers and Operations Research 32 2005 2543 2559
81. Wolpert D.H. Stacked generalization Neural Networks 5 1992 241 259
82. Xiao Z., Yang X., Pang Y., and Dang X. The prediction for listed companies' financial distress by using multiple prediction methods with rough set and Dempster-Shafer evidence theory Knowledge-Based Systems 26 2012 196 206
83. Zhang P., Zhang Z., Li A., and Shi Y. Global and local (Glocal) bagging approach for classifying noisy dataset International Journal of Software and Informatics 2 2008 181 197
84. Zhu X.Q. Lazy bagging for classifying imbalanced data Ieee icdm 2007 2007 763 768
85. Zmijewski M.E. Methodological issues related to the estimation of financial distress prediction models Journal of Accounting Research 22 1984 59 82